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Enhancement of Target-Oriented Opinion Words Extraction with Multiview-Trained Machine Reading Comprehension Model.

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This study introduces a novel question answering approach for target-oriented opinion words extraction (TOWE), improving fine-grained opinion mining by leveraging multiview learning and meta-learning for better performance.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Target-oriented opinion words extraction (TOWE) is vital for fine-grained opinion mining.
  • Current neural network methods for TOWE have limitations, including naive target representation and underutilization of latent structural knowledge.

Purpose of the Study:

  • To address limitations in current TOWE methods.
  • To improve the extraction of opinion expressions linked to specific targets.

Main Methods:

  • Formulating TOWE as a question answering (QA) problem using a machine reading comprehension (MRC) model.
  • Employing a multiview paradigm with template-based pseudo-question generation and deep attention interaction.
  • Utilizing meta-learning to aggregate knowledge from three distinct views of opinion-target structures.

Main Results:

  • Achieved new state-of-the-art results on four benchmark datasets for TOWE.
  • Demonstrated superior performance compared to existing opinion pair extraction models, including joint methods.

Conclusions:

  • The proposed QA-based multiview approach effectively enhances TOWE.
  • This method successfully extracts targeted opinions and surpasses existing models, offering a more robust solution for opinion mining.